About the Role

Join a small AI infrastructure team working to improve the performance and efficiency of large language model inference. You will build and operate systems that serve open-source models across a heterogeneous mix of hardware.

What You'll Do

Ship support for new open-source models, tuning them for latency and throughput.

Optimize inference serving systems, including batching, KV cache, speculative decoding, and quantization.

Write and tune kernels using CUDA, HIP, and Triton for a range of accelerator architectures.

Design, deploy, and operate heterogeneous clusters across hardware and cloud vendors.

Run production inference systems, improving reliability, observability, and cost efficiency.

Work with customers to understand technical needs and solve infrastructure problems.

What We're Looking For

Typically 1 to 6 years of experience in backend, infrastructure, or systems engineering, including experience at scale or ownership of a complex system end to end.

Strong understanding of computer architecture, memory hierarchy, and operating system internals beyond application-level development.

Experience with low-level or systems engineering, alongside any full-stack or web development work.

Familiarity with CUDA, Triton, HIP, or other GPU programming, and with ML inference optimization techniques such as quantization, batching, KV cache, or speculative decoding.

Experience deploying or operating large-scale infrastructure, clusters, data centers, or GPU fleets is valuable.

Strong computer science fundamentals and a track record of learning quickly, solving difficult technical problems, and using AI coding tools to improve workflows.

Comfort communicating with customers and taking ownership in a small, fast-moving team.

Compensation & Benefits

Base salary: $200,000 to $300,000 annually. Visa sponsorship is available.

Location

On-site, five days per week in San Francisco, California, United States.